{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten\n",
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import keras.backend as K\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "\n",
    "import shap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FER 2013 Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = {\n",
    "    0: 'angry',\n",
    "    1: 'disgust',\n",
    "    2: 'fear',\n",
    "    3: 'happy',\n",
    "    4: 'sad',\n",
    "    5: 'surprise',\n",
    "    6: 'neutral'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = len(class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"fer2013.csv\") as f:\n",
    "    content = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = np.array(content)\n",
    "num_of_instances = lines.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of instances:  35918\n",
      "instance length:  2304\n"
     ]
    }
   ],
   "source": [
    "print(\"number of instances: \",num_of_instances)\n",
    "print(\"instance length: \",len(lines[1].split(\",\")[1].split(\" \")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train, x_test, y_test = [], [], [], []\n",
    "\n",
    "for i in range(1,num_of_instances):\n",
    "    try:\n",
    "        emotion, img, usage = lines[i].split(\",\")\n",
    "          \n",
    "        val = img.split(\" \")\n",
    "            \n",
    "        pixels = np.array(val, 'float32')\n",
    "        \n",
    "        emotion = keras.utils.to_categorical(emotion, num_classes)\n",
    "    \n",
    "        if 'Training' in usage:\n",
    "            y_train.append(emotion)\n",
    "            x_train.append(pixels)\n",
    "        elif 'PublicTest' in usage:\n",
    "            y_test.append(emotion)\n",
    "            x_test.append(pixels)\n",
    "    except:\n",
    "        print(\"\",end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28709 train samples\n",
      "3589 test samples\n"
     ]
    }
   ],
   "source": [
    "x_train = np.array(x_train, 'float32')\n",
    "y_train = np.array(y_train, 'float32')\n",
    "x_test = np.array(x_test, 'float32')\n",
    "y_test = np.array(y_test, 'float32')\n",
    "\n",
    "x_train /= 255 #normalize inputs between [0, 1]\n",
    "x_test /= 255\n",
    "\n",
    "x_train = x_train.reshape(x_train.shape[0], 48, 48, 1)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.reshape(x_test.shape[0], 48, 48, 1)\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#construct CNN structure\n",
    "model = Sequential()\n",
    "\n",
    "#1st convolution layer\n",
    "model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1)))\n",
    "model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))\n",
    "\n",
    "#2nd convolution layer\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))\n",
    "\n",
    "#3rd convolution layer\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "#fully connected neural networks\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/serengil/tensorflow-101/blob/master/model/facial_expression_model_weights.h5\n",
    "model.load_weights('facial_expression_model_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.expand_dims(x_train, axis = 1)\n",
    "x_test = np.expand_dims(x_test, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 1, 48, 48, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def map2layer(x, layer):\n",
    "    feed_dict = dict(zip([model.layers[0].input], x.copy()))\n",
    "    return K.get_session().run(model.layers[layer].input, feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def explain(x_train, sample, layer):\n",
    "    to_explain = x_train[[sample]]\n",
    "    #print(to_explain.shape)\n",
    "    \n",
    "    e = shap.GradientExplainer(\n",
    "        (model.layers[layer].input, model.layers[-1].output),\n",
    "        map2layer(x_train, layer), \n",
    "        local_smoothing=0 # std dev of smoothing noise\n",
    "    )\n",
    "    \n",
    "    shap_values,indexes = e.shap_values(map2layer(to_explain, layer), ranked_outputs=1)\n",
    "    \n",
    "    index_names = np.vectorize(lambda x: class_names[x])(indexes)\n",
    "    \n",
    "    shap.image_plot(shap_values, to_explain[0], index_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer  0 :  <keras.layers.convolutional.Conv2D object at 0x7f08d04f84a8>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\n",
      "layer  1 :  <keras.layers.pooling.MaxPooling2D object at 0x7f08d04f8588>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\n",
      "layer  2 :  <keras.layers.convolutional.Conv2D object at 0x7f08d04f8898>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\n",
      "layer  3 :  <keras.layers.convolutional.Conv2D object at 0x7f08d04d8ef0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\n",
      "layer  4 :  <keras.layers.pooling.AveragePooling2D object at 0x7f08d04f89b0>\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\n"
     ]
    }
   ],
   "source": [
    "sample = 39\n",
    "for layer in range(0,5):\n",
    "    print(\"layer \",layer,\": \",model.layers[layer])\n",
    "    explain(x_train, sample, layer)\n",
    "    print(\"------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = 8\n",
    "explain(x_train, sample, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = 9\n",
    "explain(x_train, sample, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explain prediction in the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 1, 48, 48, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3589, 1, 48, 48, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = 100\n",
    "explain(x_test, sample, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#check prediction for that picture\\ntesting_img = np.array(x_train[sample], \\'float32\\')\\ntesting_img = testing_img.reshape([48, 48])\\n\\nplt.gray()\\nplt.imshow(testing_img)\\nplt.show()\\n\\nactual = y_train[sample]\\nprediction = model.predict(x_train[sample])\\nprint(\"Prediction: \",class_names[np.argmax(prediction)])\\nprint(\"Actual: \",class_names[np.argmax(actual)])\\n'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"#check prediction for that picture\n",
    "testing_img = np.array(x_train[sample], 'float32')\n",
    "testing_img = testing_img.reshape([48, 48])\n",
    "\n",
    "plt.gray()\n",
    "plt.imshow(testing_img)\n",
    "plt.show()\n",
    "\n",
    "actual = y_train[sample]\n",
    "prediction = model.predict(x_train[sample])\n",
    "print(\"Prediction: \",class_names[np.argmax(prediction)])\n",
    "print(\"Actual: \",class_names[np.argmax(actual)])\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 1, 48, 48, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28709, 48, 48, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rollaxis(x_train, 1)[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove 2nd dummy dimension\n",
    "x_train = np.rollaxis(x_train, 1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "background = x_train[np.random.choice(x_train.shape[0], 100, replace=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = shap.DeepExplainer(model, background)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x300 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = 39; end = 40\n",
    "shap_values = e.shap_values(x_train[begin:end])\n",
    "shap.image_plot(shap_values, -x_train[begin:end])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tabular Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['setosa', 'versicolor', 'virginica']\n",
    "feature_names = ['sepal length', 'sepal width', 'petal length', 'petal width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = pd.read_csv(\"iris-attr.data\", delimiter=\",\", names = feature_names)\n",
    "label = np.genfromtxt(\"iris-labels.data\", dtype=\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = np.unique(label).shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = keras.utils.to_categorical(label, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(4 #num of hidden units\n",
    "    , input_shape=(attributes.shape[1],))) #num of features in input layer\n",
    "model.add(Activation('sigmoid')) #activation function from input layer to 1st hidden layer\n",
    "model.add(Dense(num_classes)) #num of classes in output layer\n",
    "model.add(Activation('sigmoid')) #activation function from 1st hidden layer to output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f08e811fe80>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "model.fit(attributes, label, epochs=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  98.0 %\n"
     ]
    }
   ],
   "source": [
    "classified = 0\n",
    "\n",
    "index = 0\n",
    "for i in predictions:\n",
    "    pred = np.argmax(i)\n",
    "    actual = np.argmax(label[index])\n",
    "    \n",
    "    #print(pred, \" vs \", actual)\n",
    "    \n",
    "    if pred == actual:\n",
    "        classified = classified + 1\n",
    "    #print(\" prediction: \",pred,\" - actual: \",actual)\n",
    "    index = index + 1\n",
    "\n",
    "print(\"Accuracy: \",100*classified/len(predictions),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using 150 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.\n"
     ]
    }
   ],
   "source": [
    "explainer = shap.KernelExplainer(model.predict, attributes, link=\"logit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "babc479efa204dc6816fdc295e7ac015",
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      "/workspaces/96273/.local/lib/python3.6/site-packages/shap/explainers/kernel.py:545: UserWarning: l1_reg=\"auto\" is deprecated and in the next version (v0.29) the behavior will change from a conditional use of AIC to simply \"num_features(10)\"!\n",
      "  \"l1_reg=\\\"auto\\\" is deprecated and in the next version (v0.29) the behavior will change from a \" \\\n"
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      "\n"
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    "shap_values = explainer.shap_values(attributes, nsamples=100)"
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strict\";function r(t){this._root=t,this._startText=this.getText(),this._fallbackText=null}var i=n(3),o=n(18),a=n(172);i(r.prototype,{destructor:function(){this._root=null,this._startText=null,this._fallbackText=null},getText:function(){return\"value\"in this._root?this._root.value:this._root[a()]},getData:function(){if(this._fallbackText)return this._fallbackText;var t,e,n=this._startText,r=n.length,i=this.getText(),o=i.length;for(t=0;t<r&&n[t]===i[t];t++);var a=r-t;for(e=1;e<=a&&n[r-e]===i[o-e];e++);var u=e>1?1-e:void 0;return this._fallbackText=i.slice(t,u),this._fallbackText}}),o.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";var r=n(21),i=r.injection.MUST_USE_PROPERTY,o=r.injection.HAS_BOOLEAN_VALUE,a=r.injection.HAS_NUMERIC_VALUE,u=r.injection.HAS_POSITIVE_NUMERIC_VALUE,c=r.injection.HAS_OVERLOADED_BOOLEAN_VALUE,s={isCustomAttribute:RegExp.prototype.test.bind(new RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"),null!=e.dangerouslySetInnerHTML&&(null!=e.children&&g(\"60\"),\"object\"==typeof e.dangerouslySetInnerHTML&&z in e.dangerouslySetInnerHTML||g(\"61\")),null!=e.style&&\"object\"!=typeof e.style&&g(\"62\",r(t)))}function o(t,e,n,r){if(!(r instanceof D)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===q,u=o?i._node:i._ownerDocument;B(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;k.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;N.postMountWrapper(t)}function c(){var t=this;O.postMountWrapper(t)}function s(){var t=this;A.postMountWrapper(t)}function l(){L.track(this)}function f(){var t=this;t._rootNodeID||g(\"63\");var e=j(t);switch(e||g(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = 114"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction for  114 th instance is  [0.00029955 0.01328751 0.20471342]  ( virginica )\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction for \",sample,\"th instance is \",predictions[sample],\" (\",class_names[np.argmax(predictions[sample])],\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actual:  virginica\n"
     ]
    }
   ],
   "source": [
    "print(\"actual: \",class_names[np.argmax(label[114])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:\n",
      "sepal length    5.8\n",
      "sepal width     2.8\n",
      "petal length    5.1\n",
      "petal width     2.4\n",
      "Name: 114, dtype: float64\n",
      "-----------\n",
      "Actual: virginica\n",
      "Prediction: for  virginica\n"
     ]
    }
   ],
   "source": [
    "print(\"Features:\")\n",
    "print(attributes.iloc[sample])\n",
    "print(\"-----------\")\n",
    "print(\"Actual:\", class_names[np.argmax(label[114])])\n",
    "print(\"Prediction: for \",class_names[np.argmax(predictions[sample])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_class = np.argmax(predictions[sample])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  virginica\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iUXFTFC7K54JQXA44S0HV'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"output value\"], \"baseValue\": -1.7965344728559725, \"outValue\": -5.373073781131069, \"link\": \"identity\", \"featureNames\": [\"sepal length\", \"sepal width\", \"petal length\", \"petal width\"], \"features\": {\"0\": {\"effect\": 0.1660347155679499, \"value\": 5.8}, \"1\": {\"effect\": -0.27879823624163147, \"value\": 2.8}, \"2\": {\"effect\": -2.047851635269658, \"value\": 5.1}, \"3\": {\"effect\": -1.4159241523317574, \"value\": 2.4}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iUXFTFC7K54JQXA44S0HV')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Prediction: \",class_names[prediction_class])\n",
    "shap.force_plot(explainer.expected_value[0], shap_values[prediction_class][0,:], attributes.iloc[sample])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Many Explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "virginica\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iIWSG0G1ATMEXRQ4ANDF2'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
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\"simIndex\": 37.0, \"features\": {\"0\": {\"effect\": 0.09998831815796805, \"value\": 5.9}, \"1\": {\"effect\": -0.03890867436270434, \"value\": 3.0}, \"2\": {\"effect\": 0.23768908269636363, \"value\": 5.1}, \"3\": {\"effect\": 0.23556489447458284, \"value\": 1.8}}}], \"plot_cmap\": \"RdBu\", \"ordering_keys\": null, \"ordering_keys_time_format\": null}),\n",
       "    document.getElementById('iIWSG0G1ATMEXRQ4ANDF2')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"virginica\")\n",
    "shap.force_plot(explainer.expected_value[0], shap_values[prediction_class], attributes, link=\"logit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, attributes, plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances for  setosa\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n",
      "Feature importances for  versicolor\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n",
      "Feature importances for  virginica\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x223.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, len(class_names)):\n",
    "    current_class = class_names[i]\n",
    "    print(\"Feature importances for \",current_class)\n",
    "    shap.summary_plot(shap_values[i], attributes, plot_type=\"bar\")\n",
    "    print(\"----------------------\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
